
Data Scientist
4 weeks ago
Design, build, and maintain robust and scalable applications using Python and advanced AI frameworks.
Develop complex, multi-step workflows and autonomous systems using LLM Orchestrators like LangChain, LlamaIndex, or Semantic Kernel.
Integrate AI models and agentic systems with our existing data pipelines, APIs, and infrastructure.
Collaborate with product managers, data scientists, and software engineers to define requirements and deliver high-impact AI features.
Establish AI Development Standards: Design and implement industry best practices for building, testing, and deploying scalable AI Agents (RAG, Prompting, Chat Completion, Multi-agent, Fine-Tuning), particularly focusing on generative models and large language models (LLMs) using both proprietary and open-source frameworks.
Stay Ahead of Industry Trends: Continuously research and adopt the latest advancements in AI, ML, and generative models to drive innovation and operational improvements.
Engage in Cross-Team Collaboration: Partner with stakeholders across data science, engineering, operations, and business teams to align AI initiatives with broader organizational goals.
Train and manage the inference of deep learning models (Bert/Roberta/XL-Net/T4REC) and locally hosted LLMs (Llama/Qwen/Mistral)
Manage model training, serving and response times at scale by techniques like model quantisation, QAT (quantisation aware training), speculative decoding, flash attention etc
Required Qualifications
Bachelor's degree in Computer Science, AI, Engineering, or a related technical field.
2 - 5 years of professional software development experience with a strong proficiency in Python.
Demonstrated hands-on experience working with LLMs (e.g., GPT series, Llama, Claude, Gemini) via APIs or open-source models.
Proven experience building applications with an LLM Orchestrator framework (LangChain, LlamaIndex, etc.).
Solid understanding of AI Agent architectures and concepts like ReAct (Reasoning and Acting).
Strong analytical and problem-solving skills, with a passion for building intelligent systems.
Preferred Qualifications
Experience with cloud platforms like Azure, AWS, or GCP.
Experience with MLOps practices and tools for deploying and monitoring models in production.
Familiarity with vector databases (e.g., Pinecone, Weaviate, ChromaDB).
Active contributions to open-source AI/ML projects
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